Invisible junction features for patch recognition

ABSTRACT

The present invention uses invisible junctions which are a set of local features unique to every page of the electronic document to match the captured image to a part of an electronic document. The present invention includes: an image capture device, a feature extraction and recognition system and database. When an electronic document is printed, the feature extraction and recognition system captures an image of the document page. The features in the captured image are then extracted, indexed and stored in the database. Given a query image, usually a small patch of some document page captured by a low resolution image capture device, the features in the query image are extracted and compared against those stored in the database to identify the query image. The present invention also includes methods for feature extraction, feature indexing, feature retrieval and geometric estimation.

BACKGROUND OF THE INVENTION

The present invention relates to processing techniques for patch recognition. More particularly, the present invention relates to systems and methods for using a new feature referred to herein as invisible junctions for image-based document patch recognition.

Computers and electronic documents were once restricted to use in a desktop environments where electronic documents were output by the computer to printers and printed on paper. Printers and copiers are used in private and commercial office environments, in home environments with personal computers, and in document printing and publishing service environments. Once an electronic document is printed out on paper, manipulations on these two types of documents are mostly independent. Printing and copying technology has not been able to bridge the gap between static printed media (i.e., paper documents), and the “virtual world” of interactivity that includes the likes of digital communication, networking, information provision, advertising, entertainment and electronic commerce.

The advent and ever-increasing popularity of smaller portable computing devices and personal electronic devices, such as personal digital assistant (PDA) devices, cellular telephones (e.g., cellular camera phones) and digital cameras over the past few years, has expanded the concept of documents to include their electronic version by making the available in an electronically readable and searchable form and by introducing interactive multimedia capabilities, which are unparalleled by traditional printed media.

There continue to be problems in the prior art in bridging between the world of electronic documents on one hand and the world of paper documents on the other. A gap exists between the virtual multimedia-based world that is accessible electronically and the physical world of print media. In particular, it is still very difficult and/or computationally expensive to use a printed document to access or even find the electronic document from which the paper document was generated. A key obstacle to identifying and finding an electronic document corresponding to a printed document is the recognition of an image patch that is being captured by the camera. While capturing an image of the printed document has become trivial with the proliferation of cell phones with cameras, there is no way to use such low quality images for electronic document retrieval.

In other words, there is not an existing method that can effectively identify from a database the document page the camera is looking at, pin-point the exact camera look-at point on the recognized page, and estimate the frame box of the image on the recognized document page. This recognition task is made even more challenging considering that: 1) the input image is a small portion of the document page being looked at; 2) there is a large number of document pages in the database that look similar to each other; 3) the hand-held camera could have very different viewing conditions including different viewing angles and distances with respect to paper and camera motion due to hand movement; 4) there are considerable photometric changes due to lighting changes; and 5) there may be non-planar geometric deformation if the user is holding the paper by hand.

While there have been attempts in the prior art, they suffer from a number of deficiencies. For example, a popular Scale-Invariant Feature Transform (or SIFT) is not suitable for text documents. The SIFT key points are chosen from the extrema in scale space. More specifically, all scales and image locations are scanned for local maxima in scale space; these local maxima are chosen as key point candidates. This makes the SIFT poor at discrimination between text and the SIFT is not stable and repeatable in noisy environments. Other prior art approaches focus on geometric features of the text block but they are not suitable for Asian or ideographic languages.

SUMMARY OF THE INVENTION

The present invention overcomes the deficiencies of the prior art with a system for using invisible junctions for image-based document patch recognition. The system is advantageous because it uses invisible junctions of a low quality image patch to identify and retrieve a corresponding electronic document, page, look-at point and viewing region. The system is advantageous because it is very fast, works with both western and eastern languages, and works images that mix text and image components. The present invention uses invisible junctions which are a set of local features unique to every page of the electronic document to match the captured image to a part of an electronic document. In one embodiment, the system of the present invention includes: an image capture device, a feature extraction and recognition system and database. The feature extraction and recognition system further comprises a feature extraction unit, a feature indexing unit, a feature retrieval unit and a geometric estimation unit. When an electronic document is printed, the feature extraction and recognition system, for example in print-capture software, captures an image of the document page. The features in the captured image are then extracted, indexed and stored in the database. Given a query image, usually a small patch of some document page captured by a low resolution image capture device such as a web-cam, the features in the query image are extracted and compared against those stored in the database to identify the query image. The present invention also includes methods for feature extraction, feature indexing, feature retrieval and geometric estimation.

The features and advantages described herein are not all-inclusive and, in particular, many additional features and advantages will be apparent to one of ordinary skill in the art in view of the figures and description. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and not to limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements.

FIG. 1 is a functional diagram of an embodiment of a system for feature extraction and recognition in accordance with present invention showing a graphical representation of a piece of paper with text and images.

FIG. 2 illustrates a block diagram of an embodiment of a system configured in accordance with an embodiment of the present invention.

FIG. 3 illustrates a block diagram of a memory of the system of FIG. 2 configured in accordance with a first embodiment of the present invention.

FIG. 4 is a block diagram of an embodiment of a Feature Extraction and Recognition system of FIG. 1 in accordance with the present invention.

FIGS. 5A to 5E are graphical representations of the steps of feature extraction being performed on an example image in accordance with the present invention including an original input image, a binarization (slightly blurred image) and a distance transform (significantly burred image).

FIG. 6 is a flow chart of an embodiment of a method for performing feature extraction in accordance with the present invention.

FIG. 7A is a flow chart of an embodiment of a method for performing feature indexing in accordance with the present invention.

FIG. 7B is a flow chart of an alternate embodiment of a method for performing feature indexing in accordance with the present invention.

FIG. 8 is a flow chart of an embodiment of a method for feature retrieval in accordance with the present invention.

FIG. 9 is a flow chart of an embodiment of a method for geometric estimation in accordance with the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

A system for using a new feature referred to herein as invisible junctions for image-based document patch recognition is described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the invention.

Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

Finally, the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.

System Overview

Referring now to FIG. 1, an embodiment of a system 100 of the present invention for using a new feature, referred to herein as an invisible junction, for image-based document patch recognition is shown. The present invention uses invisible junctions, which are a set of local features as described below with reference to FIG. 5A-5D, to match the captured image to a portion of an electronic document. In this embodiment, the system 100 comprises: an image capture device 106, a feature extraction and recognition system 108 and a database 110 configured to capture an image of a portion 104 of a printed or paper document 102 and retrieve the corresponding electronic document, page, look-at point and viewing region. The correspondence between the original area 104 of the paper document 102 and the electronic representation 112 is shown by line 114. FIG. 1 illustrates an example of the system 100 capturing 116 a query image such as a small patch 104 of some paper document 102 with the image capture device 106. The feature extraction and recognition system 108 extracts the features in the query image and compares them against those stored in the database 110 to identify the electronic representation 112.

The image capture device 106 is any conventional type known in the art. For example, in the query portion of the process, the image capture device 106 may be a web camera, a camera that is part of cell phone or a digital camera. The present invention is particularly advantageous because it is operable with low resolution image capture devices. The image capture device 106 is coupled for communication with the feature extraction and recognition system 108. This communication may be via a wired or wireless network.

In another embodiment where model images are being indexed, the image captured device 106 is a scanner of a multifunction peripheral or software for generating a printed document. For example, the image captured device 106 could be print capture software in such an embodiment. These embodiments are used to index documents being output or printed by a computer (not shown).

The feature extraction and recognition system 108 performs the functions of feature extraction, indexing and retrieval for invisible junctions. The feature extraction and recognition system 108 further comprises a feature extraction unit 310, a feature indexing unit 312, a feature retrieval unit 314 and a geometric estimation unit 316 as will be described in more detail below with reference to FIGS. 2-4. The feature extraction and recognition system 108 is coupled to the image capture device 106 to receive an image of a paper document, a model image of the paper document or an image patch. The feature extraction and recognition system 108 is coupled to the database 110 to store feature descriptions of document pages and electronic document or indices to electronic documents. The feature extraction and recognition system 108 is also coupled to the database 110 to retrieve electronic document or indices to electronic documents during retrieval. The feature extraction and recognition system 108 is also coupled to output the retrieved electronic document, page, look-at point and viewing region to other computing systems for display with user interfaces.

The database 110 is a conventional type and stores indices, electronic documents, feature descriptions and other information used in the indexing and retrieval process. The database may be a federation of databases. In one embodiment, the database 110 is stored within a multifunction peripheral device.

The system 100 of the present invention is particularly advantageous because it provides a number of benefits. First, it uses a new feature, referred to herein as an invisible junction, that enable the identification and retrieval of a corresponding electronic document, page, look-at point and viewing region. The invisible junction provides stability or independence from noise and viewing condition; is repeatable in both the query image and the model image; provides strong discrimination, and is low dimensional. Second, the present invention provides a very fast recognition and retrieval at a rate of nearly 4 frames per second on a database of 10000 pages. The recognition and retrieval is fast enough for real-time interactive applications. Third, the present invention can use noisy low quality input image such as available with a cell phone camera for recognition and retrieval. In other words, the retrieval operates accurately despite image rotation, scale changes and occlusions. Fourth, the present invention can perform recognition and retrieval on both eastern and western languages. Fifth, the present invention can perform recognition and retrieval on input images that include both text and graphic images in any portion for text to image.

System Embodiments

Referring now also to FIG. 2, a functional block diagram of the feature extraction and recognition system 108 configured in accordance with an embodiment of the present invention is shown. The feature extraction and recognition system 108 preferably comprises a control unit 250, a display device 210, an input device 212, cursor control 214, a communication device 216, and one or more input/output (I/O) devices 218.

The control unit 250 comprises an arithmetic logic unit, a microprocessor, a general purpose computer or some other information appliance equipped to provide electronic display signals to display device 210. In one embodiment, the control unit 250 comprises a general purpose computer having a graphical user interface, which may be generated by, for example, a program written in Java running on top of an operating system like WINDOWS® or UNIX® based operating systems. In one embodiment, one or more application programs are executed by control unit 250 including, without limitation, drawing applications, word processing applications, electronic mail applications, financial applications and web browser applications.

Still referring to FIG. 2, the control unit 250 is shown including processor 202, main memory 204 and data storage device 206, all of which are communicatively coupled to system bus 208.

Processor 202 processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor is shown in FIG. 2, multiple processors may be included.

Main memory 204 stores instructions and/or data that may be executed by processor 202. The instructions and/or data may comprise code for performing any and/or all of the techniques described herein. Main memory 204 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, or some other memory device known in the art. The memory 204 is described in more detail below with reference to FIG. 3.

Data storage device 206 stores data and instructions for processor 202 and comprises one or more devices including a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device known in the art. In one embodiment, data storage device 206 includes database 110. In an alternate embodiment, the database is coupled to the feature extraction and recognition system 108 via communication device 216 and signal line 220 representing a conventional network.

System bus 208 represents a shared bus for communicating information and data throughout control unit 250. System bus 208 may represent one or more buses including an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, a universal serial bus (USB), or some other bus known in the art to provide similar functionality. Additional components coupled to control unit 250 through system bus 208 include the display device 210, the input device 212, cursor control 214, the communication device 216 and the I/O device(s) 218.

Display device 210 represents any device equipped to display electronic images and data as described herein. In one embodiment, the display device 210 is a liquid crystal display (LCD) and light emitting diodes (LEDs) to provide status feedback, operation settings and other information to the user. In other embodiments, the display device 210 may be, for example, a cathode ray tube (CRT) or any other similarly equipped display device, screen or monitor. In one embodiment, display device 210 is equipped with a touch screen and/or includes a digitizer in which a touch-sensitive, transparent panel covers the screen of display device 210.

In one embodiment, the input device 212 is a keyboard. The keyboard can be a QWERTY keyboard, a key pad, or representations of such created on a touch screen. Cursor control 214 represents a user input device equipped to communicate positional data as well as command selections to processor 202. Cursor control 214 may include a mouse, a trackball, a stylus, a pen, a touch screen, cursor direction keys or other mechanisms to cause movement of a cursor.

Communication device 216 links control unit 250 to a network 220 that may include multiple processing systems and in one embodiment is a network controller. The network of processing systems may comprise a local area network (LAN), a wide area network (WAN) (e.g., the Internet), and/or any other interconnected data path across which multiple devices may communicate. The control unit 250 also has other conventional connections to other systems such as a network for distribution of files (media objects) using standard network protocols such as TCP/IP, http, https, and SMTP as will be understood to those skilled in the art.

One or more I/O devices 218 are coupled to the bus 208. These I/O devices may be part of the other systems (not shown). For example, the I/O device 218 can include an image scanner for capturing an image of a document. The I/O device 218 may also includes a printer for generating documents. The I/O device 218 may also include audio input/output device equipped to receive audio input via a microphone and transmit audio output via speakers. In one embodiment, audio device is a general purpose; audio add-in/expansion card designed for use within a general purpose computer system. Optionally, I/O audio device may contain one or more analog-to-digital or digital-to-analog converters, and/or one or more digital signal processors to facilitate audio processing.

It should be apparent to one skilled in the art that feature extraction and recognition system 108 may include more or less components than those shown in FIG. 2 without departing from the spirit and scope of the present invention. For example, feature extraction and recognition system 108 may include additional memory, such as, for example, a first or second level cache, or one or more application specific integrated circuits (ASICs). Further, the feature extraction and recognition system 108 may include multiple data buses rather than the single bus 208. Multiple buses allow fast transfer of image data from and between the components of the feature extraction and recognition system 108, and simultaneous data transfer of user interface information to a display device, for example. Similarly, additional input/output devices 218 may be coupled to control unit 250 including, for example, an RFID tag reader, digital still or video cameras, or other devices that may or may not be equipped to capture and/or download electronic data to control unit 250. One or more components could also be eliminated such as the keyboard & cursor control 212.

FIG. 3 is a block diagram of one embodiment of the memory unit 204 for the feature extraction and recognition system 108. The memory unit 204 preferably comprises: an operating system 302, an application 304, a control module 306, a browser 308, a feature extraction unit 310, a feature indexing unit 312, a feature retrieval unit 314 and a geometric estimation unit 316. Those skilled in the art will recognize that the memory 204 also includes buffers for storing data and other information temporarily during the indexing or retrieval process. As noted above, the memory unit 204 stores instructions and/or data that may be executed by processor 202. The instructions and/or data comprise code for performing any and/or all of the techniques described herein. These modules 302-314 are coupled by bus 208 to the processor 202 for communication and cooperation to system 100. Those skilled in the art will recognized that while the present invention will now be described as modules or portions of a memory unit 204 of a computer, the modules or portions thereof may also be stored in other media such as permanent data storage device 206 and may be distributed across a network 220 having a plurality of different computers such as in a client/server environment.

The operating system 302 is preferably a custom operating system that is accessible to user via an application interface. In an alternate embodiment, the operating system 302 is one of a conventional type such as, WINDOWS®, SOLARIS® or LINUX® based operating systems.

The memory unit 204 also includes one or more application programs 304 including, without limitation, drawing applications, word processing applications, electronic mail applications, financial applications and web browser applications. In one embodiment, the applications 304 specifically utilize the unique capabilities of the other modules or units of memory 204 to bridge the gap between electronic documents and paper documents by enabling users to establish and identify links between a printed paper and its corresponding electronic document. More specifically, the applications 304 access electronic data from a paper and attach/embed electronic data to specific locations on a paper so that a user can later retrieval that electronic data from the paper, using cameras as the tool for the above operations. For example, one application 304 retrieves an electronic file (Microsoft Word, Adobe PDF, etc) of a printed paper in response to the user simply pointing a web-cam and capturing an image of the printed paper. Another example application 304 is a paper-based photo-wiki, which lets the user attach the pictures you have taken to specific locations on a paper map (and its electronic version), by simply pointing the camera to the desired locations on the physical paper map. There are many other applications 304 such as novel user interfaces to access or browse a document database, digitized pen and paper, map navigator, security in copiers and scanners, camera games and duplicate detection for copyright protection.

The control module 306 is used to control the other modules of the memory 204. The control module 306 is adapted for control of and communication with the application 304, the browser 308, the feature extraction unit 310, the feature indexing unit 312, the feature retrieval unit 314 and the geometric estimation unit 316. The operation of the control module 306 will be apparent from the description of FIGS. 4 and 6-9 below. While the control module 306 is shown as a separate module of the memory 204, those skilled in the art will recognize that the control module 306 in another embodiment may be distributed as routines in the other modules 310-316.

The browser 308 is a conventional type such as Internet Explorer by Microsoft® or Firefox by Mozilla. The Web browser is a software application that enables a user to display and interact with text, images, and other information typically located on a Web page at a website on the World Wide Web or a local area network.

The feature extraction unit 310 is software and routines for extracting the invisible junction features from original electronic documents and scanned images of paper documents. In one embodiment, the feature extraction unit 310 includes an input coupled to receive original electronic documents and an output to provide a feature description to the feature indexing unit 312. In another embodiment, the feature extraction unit 310 includes an input coupled to receive an image of a paper document and provide a feature description to the feature retrieval unit 314. In yet another embodiment, the feature extraction unit 310 includes a location determination module, a size determination module, an orientation determination module and a descriptor creator module coupled in series to produce the feature description. The operation of the feature extraction unit 310 is described below in detail with reference to FIGS. 5A-6. The feature extraction unit 310 is coupled for communication with the feature indexing unit 312 and the feature retrieval unit 314.

The feature indexing unit 312 is software and routines for generating one or more indices of invisible junction feature descriptions and storing them in the database 110. In one embodiment, the feature indexing unit 312 uses a quantization tree to index the feature descriptions. The feature indexing unit 312 is coupled to an output of the feature extraction unit 310 to receive invisible junction feature descriptions. The feature indexing unit 312 is coupled to the database 110. The operation of the feature indexing unit 312 is described in detail below with reference to FIGS. 7A and 7B.

The feature retrieval unit 314 is software and routines for creating a query from the invisible junction feature descriptions of a paper document and communicating with the database 110 to retrieve a document, a point and a viewing region of a matching electronic document from the database 110. The feature retrieval unit 314 is coupled to an output of the feature extraction unit 310 to receive invisible junction feature descriptions, and to the database 110 to perform queries and receive results. The possible matching documents, points and viewing regions are output by the feature retrieval unit 314. The operation of the feature retrieval unit 314 is described in detail below with reference to FIG. 8.

The geometric estimation unit 316 is software and routines for sorting the candidate documents output by the feature retrieval unit 314 and selecting a document that includes the largest number of feature points consistent with the geometric constraint. The geometric estimation unit 316 is adapted for communication with the feature retrieval unit 314 and has an output that provides a document, a point and a viewing region that matches the input image patch. The operation of the geometric estimation unit 316 is described in detail below with reference to FIG. 9A.

Referring now to FIG. 4, functional flow diagram of an embodiment of a feature extraction and recognition system 108 in accordance with the present invention is shown. The feature extraction and recognition system 108 includes two operational modes: a first mode in which the original electronic document is rendered and input and a feature index is stored for the invisible junction features from the input; and a second mode in which a image patch is input, and a feature description is created and used to retrieve a corresponding document, point and viewing region are output. For convenience and ease of understanding like reference numerals are used for like components having the same or similar functionality as that described above for other embodiments. This second embodiment of the system 108 includes the feature extraction unit 310, the feature indexing unit 312, the feature retrieval unit 314 and the geometric estimation unit 316.

FIG. 4 illustrates the flow of information between the feature extraction unit 310, the feature indexing unit 312, the feature retrieval unit 314 and the geometric estimation unit 316. In particular, the two modes of operation are show with distinct couplings 420, 430. For example, the first mode is shown with the path of double lines 420 in which the original electronic document 402 is rendered and input via signal line 420 to the feature extraction unit 310. The feature extraction unit 310 outputs feature descriptions for the invisible junctions in the document 402 to the feature indexing unit 312 via signal line 402 and the feature indexing unit 312 creates indexes and stores them in the database 110. The second mode is shown with the path of signal lines 430 in which a patch of video data or a video frame 404 is input via signal line 430 to the feature extraction unit 310. The feature extraction unit 310 outputs feature descriptions for the invisible junctions in the video frame 404 to the feature retrieval unit 314 via signal line 430. The feature retrieval unit 314 queries the database 110 via signal line 430 using the feature descriptions and outputs a list of documents having matching features to the geometric estimation unit 316 via signal line 430. The geometric estimation unit 316 applies geometric constraints to select a matching document, point and viewing region 406.

Invisible Junction Features

The present invention has created and utilizes a novel set of features for document patch recognition. The feature points used for document representation and retrieval are the junctions of the intrinsic skeleton existing in document pages. Since these junctions are not visible, i.e., there are in the blank space of the text document or regions with homogeneous colors, we refer to them here as “invisible junctions” or “invisible junction features”. There are four key components in an invisible junction feature:

-   -   Location: the coordinate (x, y) of the feature point, with the         top-left corner of the document page as the origin;     -   Orientation: for setting up a local coordinate system for each         feature point when we compute the description of each feature         point using its neighborhood pixels;     -   Scale: the size of the feature;     -   Description: the content of the feature.

Since the feature content will be determined by all of these key factors, it is critical to have them stable and repeatable. For recognition purpose, the descriptor (feature description) must also be discriminative.

Feature Extraction

Referring now to both FIGS. 5A through 5D and 6, one embodiment of a process for performing feature extraction is described. The process begins by receiving 602 a document. In one embodiment, the feature extraction unit 310 receives 602 an ideal model of an electronic document 402 and renders the model as an image. In another embodiment, the feature extraction unit 310 receives 602 a captured image of a printed document such as a video frame or web camera image 404.

Next, the process determines 604 a location for one or more invisible junctions. In one embodiment, the feature extraction unit 310 detects features at the junctions of the text document skeleton. As shown in FIGS. 5A and 5B for this embodiment, the feature extraction unit 310 binarizes the input image, and then applies a distance transform to the binary image. An example input image 501, the binarization 502 of the input image 501 and the distance transform 503 of the binarization 502 are shown in FIG. 5A. Next a skeleton 504 is detected as boundary of the label fields from the distance transformation. The skeleton 504 alternatively can be the ridge lines of the distance field. In one embodiment, ridge lines are detected as local maxima in one dimension. For the example input of FIG. 5A, the distance transform 503 and skeleton 504 are shown in FIG. 5B. The features are detected as the junction points 506 as shown in FIG. 5B. The junction points 506 alternatively can be the local maxima (in 2D) of the distance transform fields, or the local maxima on the skeleton 504. The use of invisible junctions is particularly advantageous because they are stable and repeatable. The text document contains mostly high frequency image content in the text, which are easily contaminated and hard to filter out without degrading the text. On the contrary, the white space in the text page is mostly stable and contains mostly DC components in the frequency domain. Noise in the white space can be easily filtered out without affecting the feature locations. While the distance transform field depends on the text locations which is desirable since the text content is eventually used for classification, the locations of invisible junction points 506 are robust to noise since the text around each junction are usually contaminated in similar ways.

The process next determines 606 a junction size or scale for the invisible junction. In one embodiment, the distance transformation value at each feature point 506 is intrinsic to the feature point 506. The scale of the feature point 506 is thus assigned as such distance value multiplied by a factor α. As shown in FIG. 5C in the expanded view 512, α is selected as equal to 2 for this example. The content inside the circle 510 (centered at the feature point 506 with radius equal to its scale) is invariant to camera in-plane rotation and zoom (or distance to the paper document when the camera optical axis is perpendicular to the paper). Since the feature is local, i.e., the circle is small, the feature is also robust to changes in camera viewing angles with respect to the paper (out-of-plane rotation). In other words, while global perspective distortion is apparent, locally such perspective effect is very small, and the effect on the final feature descriptor is negligible for a large range of camera out-of-plane rotations. Like the location, the scale of the invisible junction is stable and repeatable. The scale is computed based on the skeleton, and is therefore stable. However, compared to location and orientation, scale is mostly subject to the changes of the nearby texts. In one embodiment, this effect is reduced by applying a weight that is inversely-proportional to the distance value when computing the feature descriptors.

Next, the process determines 608 an orientation for the invisible junction 506. In one embodiment, each feature point 506 is assigned a local orientation. A 2D local coordinate system for each feature point 506 is then determined with the feature location as the origin and its local orientation as the x-axis. The feature descriptor is computed based on this local coordinate system. To derive the local orientation, the feature extraction unit 310 calculates a circle 511 centered at the feature point 506, with a radius determined by the feature scale. This circle 511 intersects the skeleton at several positions. The two positions that are furthest apart (from among all pairs of positions on the circle that intersect the skeleton) determine the local orientation of this feature. If there are two pairs of positions with similar distances, the feature extraction unit 310 selects the one that is closer to a horizontal orientation. FIG. 5D illustrates an example of estimation of feature orientation. The circle 511 centered at the feature point 506 with radius equal to feature scale intersects the skeleton 504 at three points A, B, and C. The two points A and B has largest distance, the feature orientation is therefore assigned as the direction of the line segment AB. Since local orientations are sensitive to noise, and some of the orientations may even be outliers, the feature extraction unit 310 makes a global orientation estimate. Specifically, feature points with local orientations vote for a global orientation for the query image. The voting is done by counting the number of local feature orientations in 360 bins (one degree for each bin). The dominant mode of the orientation histogram is the selected as the global orientation. After voting, every feature point is re-assigned its orientation to be consistent with the selected global orientation. In one embodiment, the feature extraction unit 310 uses a soft split scheme when counting the numbers for the histogram to ensure that the histogram will smoothly change when disturbed by noise.

For text patches, the global orientation is stable since there is indeed a dominant orientation, which is also true for most other non-text documents. The present invention can also be adjusted to achieve a tradeoff between pure local orientation and pure global orientation. Specifically, for each feature point, the feature extraction unit 310 can aggregate orientations from its neighbors (as compared to from all feature points in the same image) to determine its orientation. This will resolve the issue that in some languages, such as Chinese or Japanese, the document page could contain both vertical and horizontal text lines.

The orientation of a junction is determined by this skeleton which is stable as discussed above, and is therefore also stable. This is in contrast to the prior art SIFT, where the local orientation is determined by gradients of pixels around the feature point. It is well known that gradient operation amplifies noise, and therefore makes the feature orientation sensitive to noise. Our local orientation is derived from the skeleton and is much more stable. The global voting step makes the feature orientation even more stable, as it utilizes the fact that the camera is looking at a planar page, and also the fact that such page has some dominant orientation in its invisible skeleton.

Next, the process creates 610 a descriptor or feature description for the invisible junction. Referring now also to FIG. 5E, once the location, scale and orientation are determined for a feature, the feature extraction unit 310 assigns a 2D coordinate frame 516 for each feature point 506, and computes its descriptor 518 inside such coordinate frame 516. For each feature, the feature extraction unit 310 computes a square with edge length equal to two times the feature scale. This square is divided into 4×4 cells. In an alternate embodiment, the feature extraction unit 310 can use concentric circles to construct the cells. Inside each cell, the feature extraction unit 310 accumulates a weighted gradient orientation histogram that has 8 bins (i.e., 8 directions). The gradient can be calculated by several techniques including the well known Roberts or Sobel operators. The feature descriptor is the concatenation of such orientation histograms, resulting in a 128-dimensional feature vector. This feature vector is normalized to accommodate linear lighting changes.

In one embodiment, the feature extraction unit 310 applies dimension reduction scheme using Principal Component Analysis (PCA), and reserves the first 14 dimensions. While this might reduce the discriminative power of the features, it helps reduce the noise. For this embodiment, since the cells nearby the center are mostly blank, the dimension reduction is more sensible. This advantageously produces features that are intrinsically low-dimensional, considering that for each class there is only one prototype in our case. The descriptor of the present invention is particularly advantageous because it is discriminative and repeatable. Repeatability is a result of the repeatability of feature location, orientation and scale. As can be seen, the content of each feature point contains pixels from three or more words in the text document. These contents from different words are put together with some specific layout, making the descriptor very discriminative, as there are few chances for such feature point to duplicate in other places, with the same nearby words being put together with same layout. The quantization (4×4 cell and 8 directions) also make the descriptor robust to noise.

Finally, the process determines 612 whether there are more invisible junctions in the document. If so, the method selects an unprocessed junction and continues in step 604 to process it. If there are no more invisible junctions, the processing of the document is complete and ends.

Feature Indexing

As was noted above, the feature indexing unit 312 generates one or more indices of invisible junction feature descriptors and stores them in the database 110. For each model image, the feature indexing unit 312 extracts the feature points and stores them in the database 110. The feature indexing unit 312 also stores metadata associated with each feature point, including location (x, y), orientation, scale, feature descriptor and a unique ID of the document page that contains this feature point. In order to allow fast real time matching and retrieval, the feature indexing unit 312 also indexes this data for efficient storage in and retrieval from the database 110.

Referring now to FIG. 7A, one embodiment of a method for performing feature indexing in accordance with the present invention is shown. The process begins by creating 702 a quantization tree. In one embodiment, the quantization tree is trained by a set of training features using hierarchical clustering. The training set is recursively divided into K clusters. For a quantization tree with K branches and L levels, there is K^(L) leaf nodes, i.e., the vocabulary size is K^(L). For clustering the feature indexing unit 312 uses k-means algorithm with multiple random initializations. At each node of the tree, the feature indexing unit 312 records the cluster center given by the points belonging to this node. Each node of the quantization tree is also associated with a node, using term-frequency-inverse-document-frequency. The quantization tree partitions the feature space in a hierarchical way. Once the quantization tree is created, it is stored 704 in the database 110. Next, a feature point is retrieved or received 706 from the feature extraction unit 310, and is pushed down the tree and added 708 to the database. At each level of the tree, the feature point chooses the branch with a cluster center closest to the feature point. Once the feature point reaches the leaf node, an entry for this feature point is inserted into the hit list of this leaf node, recording the page ID and coordinates (x, y) of this feature point. If the page ID is already present in the hit list, the feature indexing unit 312 appends the coordinates to that existing entry, and increments the frequency of the appearance of that page ID in the hit list. Since the feature point may be at or near to the boundary between two clusters, the feature indexing unit 312 uses a soft split when pushing a feature point down the quantization tree. However this results in a large number of leaf nodes hit by such feature point. As a trade off, the feature indexing unit 312 only inserts the feature point into the closest k (k=2 for now) leaf nodes.

In an alternate embodiment, the feature indexing unit 312 does not store the original feature descriptors. Once the feature indexing unit 312 quantizes the feature space, it can approximate the feature descriptor by its index of its residing cell in the quantization space. For each feature, the feature indexing unit 312 only stores the document page ID that contains this feature, as well as the coordinate of the feature inside the document page. The storage space requires is thereby dramatically reduced.

Referring now to FIG. 7B, another embodiment of a method for performing feature indexing in accordance with the present invention is shown. In this embodiment, the feature indexing unit 312 uses use the traditional k-d tree to organize 722 the feature points. In another embodiment, the traditional k-d tree can be modified to extend the k-d tree for fast disk I/O. The k-d tree partitions the feature space into cells where each cell contains one or more feature points (actually pointers to feature points). During k-nearest neighbor search, it is often required to back-track the tree. The feature indexing unit 312 uses a depth-first linearization of the tree to order the feature points; in such a way that memory/disk locality during back-tracking is preserved. As a result, the cache miss rate is reduced (cache could be the CPU cache if the features reside in the main memory, or memory cache if the features reside in external hard disk drive), and the speed performance be increased. Then the feature indexing unit 312 uses PCA to reduce the feature dimension 724, or more exactly, to find the useful dimensions. Even though our data is intrinsically low dimensional, the low-dimensional space needs to be estimated so that the result k-d tree is well-balanced. The significance of such dimension reduction is not only to meet the low-dimension requirement, but also to order the feature dimensions in such a way that the space partition is most effective. In other words, the space cut is ordered 726 in such a way that the dimension with most energy will be cut first, so that the system 108 can achieve a k-d tree that is not only balanced, but also with cell boundaries that are stable (i.e., small noises will not disturb which cell should the feature belong to).

Feature Retrieval and Geometric Estimation

Referring now to FIG. 8, an embodiment of a method for feature retrieval in accordance with the present invention is shown.

Where indexing is done in accordance with the first embodiment described above with reference to FIG. 7A, the feature retrieval unit 314 sends the feature points detected in the query image down to the quantization, and collects the hit lists at the leaf nodes. This produces a table of candidate document pages, along with a list of coordinates (x, y) for each candidate page. This can be send to the geometric estimation unit for further processing.

In the alternate embodiment of indexing in FIG. 7B, the retrieval process begins with the feature retrieval unit 314 receiving 802 a feature descriptor from the feature extraction unit 310. The feature retrieval unit 314 performs pattern recognition based on local features. The feature retrieval unit 314 searches for the page document whose feature points best corresponded to the feature points of the given query image patch. To establish the correspondence (in other words to recognize), the feature retrieval unit 314 recognizes each feature point in the given image. More specifically, the feature retrieval unit 314 searches 804 the database 110 for the k nearest neighbors (kNN) of a feature point from the query image. The collection of all such points from the database is then divided 806 into sets where points in one set share the same document page ID (recorded as meta-data with the feature point in the database). So for each candidate document page, the feature retrieval unit 314 produces a set of feature correspondences. Note that many of such correspondences are outliers, since for each query point there is at most one true correspondence in its k nearest neighbors.

Those skilled in the art will recognize that various values of k can be used. In one example embodiment, k=10. Other values of k can be used in order to reduce the query time. On alternate method also monitors the distance between the query feature and the current neighbor. Once there is a big jump in the distance, the feature retrieval unit 314 returns k<10 neighbors as the following neighbors are probably outliers (i.e., the true correspondence is probably already in the current k<10 points).

In yet another embodiment, the feature retrieval unit 314 may include scoring using tree-intersection. For each query image, the feature retrieval unit 314 includes a tree representation given the paths that the query points have gone through. The feature retrieval unit 314 constructs a similar tree for each candidate page. The similarity of the query image and a model image is then computed based on the intersection of the two trees. Thereafter the tree intersections can be ranking.

FIG. 9 is a flow chart of an embodiment of a method for geometric estimation in accordance with the present invention. Once all the candidate page identifications have been identified by the feature retrieval unit 314, they are sent to the geometric estimation unit for further to rank all candidate document page identifications to generate the final query result. The geometric estimation unit 316 begins by sorting 902 the candidate documents using the number of feature points that each candidate document page has. Then the geometric estimation unit 316 ranks the candidates using geometric constraints. For example, the geometric estimation unit 316 selects the document that contains the largest number of feature points consistent with the geometric constraint of the input image is a plane, therefore there is a transformation between the paper plane and the camera imaging plane. Finally, the geometric estimation unit 316 outputs 906 the matching document identification and page, point and viewing region.

The foregoing description of the embodiments of the present invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the present invention be limited not by this detailed description, but rather by the claims of this application. As will be understood by those familiar with the art, the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies and other aspects are not mandatory or significant, and the mechanisms that implement the present invention or its features may have different names, divisions and/or formats. Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies and other aspects of the present invention can be implemented as software, hardware, firmware or any combination of the three. Also, wherever a component, an example of which is a module, of the present invention is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming. Additionally, the present invention is in no way limited to implementation in any specific programming language, or for any specific operating system or environment. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the present invention, which is set forth in the following claims. 

1. A computer-implemented method for generating invisible junctions, the method comprising: receiving, with a processor, an electronic document; applying, with the processor, a distance transformation to the electronic document to generate a distance transform; detecting, with the processor, a skeleton in the distance transform; determining, with the processor, junction points in the skeleton as the invisible junctions, the junction points having a distance transformation value based on the distance transform; determining, with the processor, a junction size for at least one junction point, the junction size based at least in part on the distance transformation value; and creating, with the processor, a feature descriptor for each invisible junction, the feature descriptor including the junction size.
 2. The method of claim 1 wherein: determining the junction points includes determining a junction location for each junction point; and the feature descriptor includes the junction location.
 3. The method of claim 1 wherein the junction size is the distance transformation value multiplied by a factor.
 4. The method of claim 1 wherein: determining the junction points includes determining a junction orientation for each junction point; and the feature descriptor includes the junction orientation.
 5. The method of claim 1 wherein the junction points are at least one of a local maxima of a distance transform field and a local maxima on the skeleton.
 6. A computer-implemented method for generating and storing invisible junctions, the method comprising: receiving, with a processor, an electronic document; applying, with the processor, a distance transformation to the electronic document to generate a distance transform; detecting, with the processor, a skeleton in the distance transform; determining, with the processor, junction points in the skeleton as the invisible junctions, the junction points having a distance transformation value based on the distance transform; determining, with the processor, a junction size for at least one junction point, the junction size based at least in part on the distance transformation value; extracting, with the processor, an invisible junction feature descriptor from the electronic document for each invisible junction, the invisible junction feature descriptor including the including the junction size; creating, with the processor, a feature index from the invisible junction feature descriptors; and storing the feature index.
 7. The method of claim 6, wherein storing the feature index comprises storing the feature index in a database.
 8. The method of claim 6, wherein storing the feature index comprises storing the feature index and the invisible junction feature descriptors in a database.
 9. The method of claim 6, wherein creating the feature index from the invisible junction feature descriptors comprises: creating a quantization tree; and adding a feature point to the quantization tree for the invisible junction feature descriptors.
 10. The method of claim 9, wherein the quantization tree and the feature point are stored in a database.
 11. The method of claim 9, further comprising: extracting an additional invisible junction feature descriptor; and adding a second feature point to the quantization tree for the additional invisible junction feature descriptor.
 12. The method of claim 11, wherein the quantization tree is trained by a set of training features using hierarchical clustering.
 13. The method of claim 11, wherein the feature point is added to the quantization tree by adding a leaf node including a page ID and coordinates (x, y) of the feature point.
 14. The method of claim 6, wherein creating the feature index from the invisible junction feature descriptors comprises: organizing a feature point into a k-d tree; reducing a feature dimension using principal component analysis; and ordering the feature dimension to effectively partition the k-d tree.
 15. A system for generating invisible junctions, the system comprising: a processor; a feature determination unit stored on a memory and executable by the processor, the feature determination unit having an input and an output for applying a distance transformation to an electronic document to generate a distance transform, for detecting a skeleton in the distance transform, for determining junction points in the skeleton as the invisible junctions, the junction points having a distance transformation value based on the distance transform and for determining a junction size that is based at least in part on the distance transformation value, the input of the feature determination unit coupled to receive the electronic document; and a descriptor creation unit stored on the memory and executable by the processor, the descriptor creation unit having an input and an output for creating a feature descriptor for each invisible junction, the feature descriptor including the junction size, the input of the descriptor creation unit coupled to the output of the feature determination unit to receive information about the invisible junctions, the output of the descriptor creation unit providing the feature descriptors.
 16. The system of claim 15, further comprising a database to store the feature descriptors, the output of the descriptor creation unit coupled to the database.
 17. The system of claim 15, wherein the feature determination unit comprises a location determination module having an input and an output for determining a junction location, the input of the location determination module coupled to receive the electronic document, the output of the location determination module coupled to the input of the descriptor creation unit.
 18. The system of claim 17, wherein the feature determination unit comprises a size determination module having an input and an output for determining the junction size, the input of the size determination module coupled to the output of the location determination module to receive the junction location, the output of the size determination module coupled to the input of the descriptor creation unit.
 19. The system of claim 18, wherein the feature determination unit comprises an orientation determination module having an input and an output for determining a junction orientation, the input of the orientation determination module coupled to the output of the size determination module to receive the junction size, the output of the orientation determination module coupled to the input of the descriptor creation unit.
 20. A system for generating invisible junctions, the system comprising: a processor; a feature extraction unit stored on a memory and executable by the processor, the feature extraction unit having an input and an output for applying a distance transformation to an electronic document to generate a distance transform, for detecting a skeleton in the distance transform, for determining junction points in the skeleton as the invisible junctions, the junction points having a distance transformation value based on the distance transform, for determining a junction size based on the distance transformation value and for producing an invisible junction feature descriptor for each invisible junction, the invisible junction feature descriptor including the junction size, the input of the feature extraction unit coupled to receive the electronic document; and a feature indexing unit stored on the memory and executable by the processor, the feature indexing unit having an input and an output for creating a feature index for the invisible junctions, the input of the feature indexing unit coupled to the output of the feature extraction unit to receive the invisible junction feature descriptors, the output of the feature indexing unit providing the feature index.
 21. The system of claim 20, further comprising a database to store the feature index, the output of the feature indexing unit coupled to the database.
 22. The system of claim 20, wherein the feature extraction unit creates the invisible junction feature descriptors for the invisible junctions.
 23. The system of claim 20, wherein the feature extraction unit comprises: a location determination module having an input and an output for determining a junction location, the input of the location determination module coupled to receive the electronic document; a size determination module having an input and an output for determining the junction size, the input of the size determination module coupled to the output of the location determination module to receive the junction location; an orientation determination module having an input and an output for determining a junction orientation, the input of the orientation determination module coupled to the output of the size determination module to receive the junction size; and wherein the feature extraction unit is coupled to the feature indexing unit to provide the junction location, junction size and junction orientation.
 24. The system of claim 20, wherein the feature indexing unit also provides the invisible junction feature descriptors.
 25. The system of claim 20, wherein the feature indexing unit creates a quantization tree and adds a feature point to the quantization tree for the invisible junction feature descriptors.
 26. The system of claim 25, wherein the feature indexing unit adds the feature point by adding a leaf node including a page ID and coordinates (x, y) of the feature point.
 27. The system of claim 20, wherein the feature descriptor includes a feature point and the feature indexing unit organizes the feature point into a k-d tree, reduces a feature dimension of the k-d tree using principal component analysis; and orders the feature dimension to effectively partition the k-d tree. 